Predicting Missing Links via Local Information

TL;DR

This paper evaluates local similarity measures for missing link prediction in networks, finding that simple measures like common neighbors perform well, and proposing new measures that improve accuracy by considering extended neighbor information.

Contribution

The paper introduces a new similarity measure based on resource allocation and extends it to include next nearest neighbors, significantly improving link prediction accuracy.

Findings

Common neighbors performs best among nine measures.

The proposed resource allocation-based measure outperforms common neighbors.

Using next nearest neighbors enhances prediction accuracy.

Abstract

Missing link prediction of networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely common neighbors, has the best overall performance, and the Adamic-Adar index performs the second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbors. It is found that many links are assigned same scores if only the information of the nearest neighbors is used. We therefore design another new measure exploited information of the next nearest neighbors, which can remarkably enhance the prediction accuracy.